Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom
Background blurb about emissions, retofit, carbon tax/levy etc
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
We will use a number of datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
This analysis is at LSOA level.
Load lSOA look-up table. This covers all countries/regions of the UK but some variables are not defined in some countries.
## Loading LSOA look-up table with useful labels
## [1] 42619
| Name | data$lsoa_lookup |
| Number of rows | 42619 |
| Number of columns | 22 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| character | 21 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| LSOA11CD | 0 | 1 | 8 | 9 | 0 | 42619 | 0 |
| LSOA11NM | 0 | 1 | 0 | 33 | 7866 | 34754 | 0 |
| MSOA11CD | 0 | 1 | 0 | 9 | 7866 | 7202 | 0 |
| MSOA11NM | 0 | 1 | 0 | 32 | 7866 | 7202 | 0 |
| LA11CD | 0 | 1 | 0 | 9 | 7866 | 337 | 0 |
| LA11NM | 0 | 1 | 0 | 35 | 7866 | 337 | 0 |
| WD20CD | 0 | 1 | 0 | 9 | 7866 | 8024 | 0 |
| WD20NM | 0 | 1 | 0 | 53 | 7866 | 7438 | 0 |
| LAD20CD | 0 | 1 | 0 | 9 | 7866 | 337 | 0 |
| LAD20NM | 0 | 1 | 0 | 35 | 7866 | 337 | 0 |
| i.LSOA11NM | 0 | 1 | 0 | 33 | 7866 | 34754 | 0 |
| RUC11CD | 0 | 1 | 0 | 2 | 7866 | 9 | 0 |
| RUC11 | 0 | 1 | 0 | 47 | 7866 | 9 | 0 |
| SOA Code | 0 | 1 | 8 | 9 | 0 | 42619 | 0 |
| SOA Name | 0 | 1 | 3 | 63 | 0 | 42465 | 0 |
| LA Code | 0 | 1 | 9 | 9 | 0 | 391 | 0 |
| LA Name | 0 | 1 | 4 | 36 | 0 | 391 | 0 |
| Region/Country | 0 | 1 | 4 | 24 | 0 | 12 | 0 |
| Supergroup Name | 0 | 1 | 15 | 35 | 0 | 8 | 0 |
| Group Code | 0 | 1 | 2 | 2 | 0 | 24 | 0 |
| Group Name | 0 | 1 | 12 | 35 | 0 | 24 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Supergroup Code | 0 | 1 | 4.76 | 2.1 | 1 | 3 | 5 | 7 | 8 | ▃▃▇▁▆ |
##
## East East Midlands London
## 0 0 0
## Rural town and fringe 544 417 4
## Rural town and fringe in a sparse setting 19 4 0
## Rural village and dispersed 448 273 4
## Rural village and dispersed in a sparse setting 12 11 0
## Urban city and town 2232 1540 17
## Urban city and town in a sparse setting 0 6 0
## Urban major conurbation 359 20 4810
## Urban minor conurbation 0 503 0
## <NA> 0 0 0
##
## North East North West
## 0 0
## Rural town and fringe 201 235
## Rural town and fringe in a sparse setting 20 21
## Rural village and dispersed 48 147
## Rural village and dispersed in a sparse setting 22 38
## Urban city and town 620 1550
## Urban city and town in a sparse setting 9 11
## Urban major conurbation 737 2495
## Urban minor conurbation 0 0
## <NA> 0 0
##
## Northern Ireland Scotland
## 890 6976
## Rural town and fringe 0 0
## Rural town and fringe in a sparse setting 0 0
## Rural village and dispersed 0 0
## Rural village and dispersed in a sparse setting 0 0
## Urban city and town 0 0
## Urban city and town in a sparse setting 0 0
## Urban major conurbation 0 0
## Urban minor conurbation 0 0
## <NA> 0 0
##
## South East South West Wales
## 0 0 0
## Rural town and fringe 579 419 252
## Rural town and fringe in a sparse setting 0 29 78
## Rural village and dispersed 495 493 129
## Rural village and dispersed in a sparse setting 0 51 147
## Urban city and town 3848 2274 1268
## Urban city and town in a sparse setting 0 15 35
## Urban major conurbation 460 0 0
## Urban minor conurbation 0 0 0
## <NA> 0 0 0
##
## West Midlands
## 0
## Rural town and fringe 228
## Rural town and fringe in a sparse setting 5
## Rural village and dispersed 264
## Rural village and dispersed in a sparse setting 19
## Urban city and town 1381
## Urban city and town in a sparse setting 9
## Urban major conurbation 1581
## Urban minor conurbation 0
## <NA> 0
##
## Yorkshire and The Humber <NA>
## 0 0
## Rural town and fringe 310 0
## Rural town and fringe in a sparse setting 21 0
## Rural village and dispersed 189 0
## Rural village and dispersed in a sparse setting 28 0
## Urban city and town 994 0
## Urban city and town in a sparse setting 9 0
## Urban major conurbation 1061 0
## Urban minor conurbation 705 0
## <NA> 0 0
Labeled as 2019 but actually 2018 data. Source: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019
## Overall IMD decile counts
## [1] 32844
##
## 1 (10% most deprived) 2 3
## 3284 3284 3285
## 4 5 6
## 3284 3285 3284
## 7 8 9
## 3284 3285 3284
## 10 (10% least deprived)
## 3285
## # Southampton IMD decile counts
## [1] 32844
##
## 1 (10% most deprived) 2 3
## 3284 3284 3285
## 4 5 6
## 3284 3285 3284
## 7 8 9
## 3284 3285 3284
## 10 (10% least deprived)
## 3285
##
## 1 (10% most deprived) 2 3
## 0.09998782 0.09998782 0.10001827
## 4 5 6
## 0.09998782 0.10001827 0.09998782
## 7 8 9
## 0.09998782 0.10001827 0.09998782
## 10 (10% least deprived)
## 0.10001827
##
## 50% least deprived 50% most deprived
## 16422 16422
##
## 50% least deprived 50% most deprived
## 0.5 0.5
These are LSOA level deprivation indices. Decile is the English & Welsh decile:
2019 estimates - do we actually use this data?
Source: https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2021
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
## [1] 32844
| Name | credsLsoaDT |
| Number of rows | 32844 |
| Number of columns | 96 |
| Key | LSOA11CD |
| _______________________ | |
| Column type frequency: | |
| character | 16 |
| factor | 1 |
| numeric | 79 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| lsoa11cd | 0 | 1 | 9 | 9 | 0 | 32844 | 0 |
| lsoa11nm | 0 | 1 | 9 | 33 | 0 | 32844 | 0 |
| lsoa11nmw | 0 | 1 | 9 | 33 | 0 | 32844 | 0 |
| LSOA01NM | 0 | 1 | 9 | 33 | 0 | 32844 | 0 |
| LADcd | 0 | 1 | 9 | 9 | 0 | 317 | 0 |
| LADnm | 0 | 1 | 4 | 35 | 0 | 317 | 0 |
| LAD11NM | 0 | 1 | 4 | 35 | 0 | 317 | 0 |
| IMD_50pc | 0 | 1 | 17 | 18 | 0 | 2 | 0 |
| LSOA11CD | 0 | 1 | 9 | 9 | 0 | 32844 | 0 |
| LSOA11NM | 0 | 1 | 9 | 33 | 0 | 32844 | 0 |
| WD20CD | 0 | 1 | 9 | 9 | 0 | 7180 | 0 |
| RUC11 | 0 | 1 | 19 | 47 | 0 | 8 | 0 |
| oacSuperGroupName | 0 | 1 | 15 | 35 | 0 | 8 | 0 |
| region | 0 | 1 | 4 | 24 | 0 | 9 | 0 |
| i.LAD11NM | 0 | 1 | 4 | 28 | 0 | 326 | 0 |
| WD18NM | 0 | 1 | 3 | 56 | 0 | 6786 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| IMD_Decile_label | 0 | 1 | FALSE | 10 | 3: 3285, 5: 3285, 8: 3285, 10 : 3285 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| FID | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| st_areasha | 0 | 1.00 | 3971258.76 | 13341704.03 | 16901.71 | 272676.31 | 455693.68 | 1285547.87 | 6.837843e+08 | ▇▁▁▁▁ |
| st_lengths | 0 | 1.00 | 8047.21 | 10508.84 | 681.05 | 3117.18 | 4282.31 | 7239.18 | 1.693713e+05 | ▇▁▁▁▁ |
| IMD_Rank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| IMD_Decile | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| IMDScore | 0 | 1.00 | 21.67 | 15.33 | 0.54 | 9.91 | 17.65 | 29.58 | 9.274000e+01 | ▇▅▂▁▁ |
| IMDRank0 | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| IMDDec0 | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| IncScore | 0 | 1.00 | 0.13 | 0.09 | 0.00 | 0.06 | 0.10 | 0.18 | 6.100000e-01 | ▇▃▂▁▁ |
| IncRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| IncDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| EmpScore | 0 | 1.00 | 0.10 | 0.07 | 0.00 | 0.05 | 0.08 | 0.13 | 5.300000e-01 | ▇▃▁▁▁ |
| EmpRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| EmpDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| EduScore | 0 | 1.00 | 21.69 | 18.61 | 0.01 | 7.36 | 16.18 | 30.91 | 9.945000e+01 | ▇▃▂▁▁ |
| EduRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| EduDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| HDDScore | 0 | 1.00 | 0.00 | 0.86 | -3.21 | -0.59 | -0.03 | 0.58 | 3.550000e+00 | ▁▃▇▂▁ |
| HDDRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| HDDDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| CriScore | 0 | 1.00 | 0.00 | 0.82 | -3.46 | -0.56 | 0.02 | 0.56 | 3.350000e+00 | ▁▂▇▃▁ |
| CriRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| CriDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| BHSScore | 0 | 1.00 | 21.69 | 10.71 | 0.48 | 13.66 | 20.20 | 28.27 | 7.046000e+01 | ▅▇▃▁▁ |
| BHSRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| BHSDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| EnvScore | 0 | 1.00 | 21.69 | 15.20 | 0.13 | 9.45 | 18.51 | 31.08 | 9.160000e+01 | ▇▆▂▁▁ |
| EnvRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| EnvDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| IDCScore | 0 | 1.00 | 0.16 | 0.12 | 0.00 | 0.06 | 0.13 | 0.23 | 9.000000e-01 | ▇▃▁▁▁ |
| IDCRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| IDCDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| IDOScore | 0 | 1.00 | 0.17 | 0.12 | 0.01 | 0.07 | 0.13 | 0.23 | 9.900000e-01 | ▇▃▁▁▁ |
| IDORank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| IDODec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| CYPScore | 0 | 1.00 | 0.00 | 0.80 | -2.79 | -0.55 | -0.02 | 0.55 | 3.400000e+00 | ▁▅▇▂▁ |
| CYPRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| CYPDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| ASScore | 0 | 1.00 | 0.31 | 0.11 | 0.03 | 0.22 | 0.30 | 0.38 | 7.500000e-01 | ▂▇▆▂▁ |
| ASRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| ASDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| GBScore | 0 | 1.00 | 0.00 | 0.79 | -2.76 | -0.54 | -0.04 | 0.48 | 3.260000e+00 | ▁▅▇▂▁ |
| GBRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| GBDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| WBScore | 0 | 1.00 | 0.00 | 2.49 | -8.45 | -1.75 | -0.23 | 1.60 | 7.660000e+00 | ▁▃▇▃▁ |
| WBRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| WBDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| IndScore | 0 | 1.00 | 0.00 | 0.84 | -3.36 | -0.56 | 0.00 | 0.58 | 2.960000e+00 | ▁▂▇▅▁ |
| IndRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| IndDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| OutScore | 0 | 1.00 | 0.00 | 0.82 | -3.75 | -0.58 | -0.07 | 0.52 | 3.310000e+00 | ▁▂▇▃▁ |
| OutRank | 0 | 1.00 | 16422.50 | 9481.39 | 1.00 | 8211.75 | 16422.50 | 24633.25 | 3.284400e+04 | ▇▇▇▇▇ |
| OutDec | 0 | 1.00 | 5.50 | 2.87 | 1.00 | 3.00 | 5.50 | 8.00 | 1.000000e+01 | ▇▇▇▇▇ |
| TotPop | 0 | 1.00 | 1666.31 | 363.62 | 523.00 | 1446.00 | 1598.00 | 1800.00 | 9.551000e+03 | ▇▁▁▁▁ |
| DepChi | 0 | 1.00 | 316.80 | 117.76 | 17.00 | 238.00 | 298.00 | 372.00 | 1.632000e+03 | ▇▃▁▁▁ |
| Pop16_59 | 0 | 1.00 | 965.48 | 306.56 | 310.00 | 784.00 | 907.00 | 1074.00 | 8.608000e+03 | ▇▁▁▁▁ |
| Pop60_ | 0 | 1.00 | 384.02 | 151.87 | 15.00 | 276.00 | 367.00 | 471.00 | 1.372000e+03 | ▃▇▂▁▁ |
| WorkPop | 0 | 1.00 | 971.46 | 304.15 | 329.25 | 793.25 | 910.75 | 1076.00 | 8.588750e+03 | ▇▁▁▁▁ |
| Shape__Area | 0 | 1.00 | 10764015.16 | 37736149.72 | 43529.25 | 730662.72 | 1230811.81 | 3473220.26 | 2.098972e+09 | ▇▁▁▁▁ |
| Shape__Length | 0 | 1.00 | 13198.60 | 17330.35 | 1094.76 | 5107.38 | 7029.59 | 11916.91 | 2.974864e+05 | ▇▁▁▁▁ |
| CREDStotal_kgco2e | 0 | 1.00 | 13700570.11 | 5146064.38 | 4451600.00 | 9623500.00 | 13444900.00 | 16808000.00 | 2.366700e+08 | ▇▁▁▁▁ |
| CREDSgas_kgco2e2018 | 805 | 0.98 | 1764087.16 | 562937.11 | 4110.90 | 1429190.00 | 1723600.00 | 2059200.00 | 6.798010e+06 | ▂▇▁▁▁ |
| CREDSelec_kgco2e2018 | 0 | 1.00 | 761673.41 | 261403.26 | 21004.00 | 596490.00 | 690300.00 | 848175.00 | 4.586400e+06 | ▇▂▁▁▁ |
| CREDSotherEnergy_kgco2e2011 | 0 | 1.00 | 168652.27 | 356823.87 | 0.00 | 28362.00 | 51800.00 | 108112.50 | 3.840000e+06 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2011 | 805 | 0.98 | 2775450.91 | 824951.54 | 674000.80 | 2239496.00 | 2602296.00 | 3112414.00 | 1.190016e+07 | ▇▃▁▁▁ |
| CREDScar_kgco2e2018 | 0 | 1.00 | 1594080.93 | 702539.82 | 97520.00 | 1113480.00 | 1509600.00 | 1964700.00 | 1.272180e+07 | ▇▁▁▁▁ |
| CREDSvan_kgco2e2018 | 2 | 1.00 | 273550.72 | 1976936.38 | 28.52 | 96537.00 | 154530.00 | 261690.00 | 2.237900e+08 | ▇▁▁▁▁ |
| pop_2018 | 0 | 1.00 | 1704.35 | 426.91 | 591.00 | 1450.00 | 1620.00 | 1850.00 | 1.470000e+04 | ▇▁▁▁▁ |
| energy_pc | 805 | 0.98 | 21.06 | 6.71 | 1.09 | 16.06 | 19.80 | 25.47 | 5.618000e+01 | ▁▇▃▁▁ |
| pc_Heating_Electric | 0 | 1.00 | 7.80 | 8.71 | 0.00 | 2.54 | 4.96 | 9.85 | 9.028000e+01 | ▇▁▁▁▁ |
| epc_total | 0 | 1.00 | 434.07 | 188.87 | 30.00 | 315.00 | 390.00 | 503.00 | 6.350000e+03 | ▇▁▁▁▁ |
| epc_newbuild | 0 | 1.00 | 79.02 | 121.92 | 0.00 | 25.00 | 44.00 | 85.00 | 5.840000e+03 | ▇▁▁▁▁ |
| epc_A | 0 | 1.00 | 0.78 | 4.35 | 0.00 | 0.00 | 0.00 | 0.00 | 3.080000e+02 | ▇▁▁▁▁ |
| epc_B | 0 | 1.00 | 51.25 | 107.67 | 0.00 | 7.00 | 19.00 | 52.00 | 5.770000e+03 | ▇▁▁▁▁ |
| epc_C | 0 | 1.00 | 124.24 | 88.84 | 1.00 | 64.00 | 101.00 | 159.00 | 1.220000e+03 | ▇▁▁▁▁ |
| epc_D | 0 | 1.00 | 170.41 | 53.69 | 0.00 | 136.00 | 163.00 | 197.00 | 7.330000e+02 | ▅▇▁▁▁ |
| epc_E | 0 | 1.00 | 67.91 | 37.79 | 0.00 | 41.00 | 62.00 | 87.00 | 3.630000e+02 | ▇▅▁▁▁ |
| epc_F | 0 | 1.00 | 15.29 | 16.57 | 0.00 | 6.00 | 11.00 | 19.00 | 2.660000e+02 | ▇▁▁▁▁ |
| epc_G | 0 | 1.00 | 4.22 | 6.63 | 0.00 | 1.00 | 2.00 | 5.00 | 1.320000e+02 | ▇▁▁▁▁ |
##
## Adur Allerdale Amber Valley Arun Ashfield Ashford
## 42 60 78 94 74 78
## region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: East 3614 8952.239 3111.628
## 2: East Midlands 2774 7863.378 3190.287
## 3: London 4835 9117.328 3137.922
## 4: North East 1657 6827.127 4106.095
## 5: North West 4497 7444.258 2859.758
## 6: South East 5382 9871.440 3662.890
## 7: South West 3281 7987.486 2758.576
## 8: West Midlands 3487 7562.679 4014.465
## 9: Yorkshire and The Humber 3317 7449.515 2772.344
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOAs (check):
## [1] 32844
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: <NA> <NA> 83748 34561 NA
## 2: Aylesbury Vale 012A Riverside 3373 3175 3110
## 3: Test Valley 003B St Mary's 2641 2487 2230
## 4: Milton Keynes 017H Broughton 2517 2382 2460
## 5: Test Valley 003A Alamein 2513 2638 2350
## 6: Peterborough 019D Stanground South 2261 2178 1880
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: <NA> <NA> 83748 34561 NA
## 2: Newham 013G Stratford and New Town 731 6351 6350
## 3: Wandsworth 002B Queenstown 675 3282 1700
## 4: Aylesbury Vale 012A Riverside 3373 3175 3110
## 5: Newham 037E Royal Docks 574 3116 2900
## 6: Lewisham 012E Lewisham Central 568 2893 2730
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A Riverside 3373 3175 3110
## 2: Test Valley 003B St Mary's 2641 2487 2230
## 3: Milton Keynes 017H Broughton 2517 2382 2460
## 4: Test Valley 003A Alamein 2513 2638 2350
## 5: Peterborough 019D Stanground South 2261 2178 1880
## 6: Swindon 008B Blunsdon and Highworth 2227 2166 2020
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
That assumption seems sensible…
Note very high number of meters in Newham…?
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 32039 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 19490.24 | 9188.71 | 3587.62 | 12947.16 | 18275.82 | 24069.71 | 586372.22 | ▇▁▁▁▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2465.42 | 851.99 | 3.92 | 2037.62 | 2434.68 | 2868.68 | 71095.56 | ▇▁▁▁▁ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1021.63 | 220.17 | 40.55 | 888.82 | 977.15 | 1092.44 | 4046.23 | ▂▇▁▁▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3487.05 | 912.74 | 458.61 | 2978.41 | 3398.85 | 3894.92 | 72698.53 | ▇▁▁▁▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 175.08 | 336.37 | 0.00 | 40.20 | 69.74 | 136.09 | 6877.09 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3662.13 | 910.23 | 912.57 | 3125.71 | 3558.65 | 4082.50 | 76436.03 | ▇▁▁▁▁ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 2200.93 | 1038.37 | 127.70 | 1529.01 | 2142.16 | 2797.44 | 89700.00 | ▇▁▁▁▁ |
| CREDSvan_kgco2e2018_pdw | 1 | 1 | 366.16 | 2774.12 | 0.05 | 137.01 | 217.71 | 342.60 | 344822.80 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 1 | 1 | 2567.13 | 2987.05 | 141.80 | 1742.05 | 2422.45 | 3151.56 | 346819.80 | ▇▁▁▁▁ |
Examine patterns of per dwelling emissions for sense.
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDStotal_kgco2e_pdw
## t = -123.51, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5753081 -0.5604715
## sample estimates:
## cor
## -0.5679359
## LSOA11CD WD18NM All_Tco2e_per_dw
## Length:32039 Length:32039 Min. : 3.588
## Class :character Class :character 1st Qu.: 12.947
## Mode :character Mode :character Median : 18.276
## Mean : 19.490
## 3rd Qu.: 24.070
## Max. :586.372
## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01031998 Durrington and Larkhill 586.3722
## 2: E01009320 Sheldon 364.6687
## 3: E01033484 Park East 203.6630
## 4: E01010151 Knowle 171.2150
## 5: E01019556 Holmebrook 160.1703
## 6: E01033749 Greenbank 139.6909
## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01004562 Queenstown 4.965387
## 2: E01005133 Ancoats & Beswick 4.906386
## 3: E01008703 Hendon 4.369222
## 4: E01015895 Victoria 4.289301
## 5: E01033726 Eltham West 3.808630
## 6: E01033583 Stratford and New Town 3.587624
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use. This preserves the negative correlation shown in the previou splot for ‘all emissions’ but with some variation, notably for LSOAs which have a higher % ofelectric heating.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.92 2037.62 2434.68 2465.42 2868.68 71095.56
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSgas_kgco2e2018_pdw
## t = -70.089, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3740796 -0.3550910
## sample estimates:
## cor
## -0.3646232
Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use. This is mnuch more random… although note the LSOAs with higher % electric heating.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4069829 -0.3885483
## sample estimates:
## cor
## -0.3978058
Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4069829 -0.3885483
## sample estimates:
## cor
## -0.3978058
## RUC11 mean_gas_kgco2e
## 1: Rural town and fringe 2536.798
## 2: Rural town and fringe in a sparse setting 2254.050
## 3: Rural village and dispersed 1879.326
## 4: Rural village and dispersed in a sparse setting 1015.146
## 5: Urban city and town 2456.035
## 6: Urban city and town in a sparse setting 2230.231
## 7: Urban major conurbation 2552.187
## 8: Urban minor conurbation 2582.837
## mean_elec_kgco2e mean_other_energy_kgco2e
## 1: 1083.0125 274.22605
## 2: 993.6811 271.63854
## 3: 1481.8790 1131.91956
## 4: 1405.2387 1440.13693
## 5: 991.9263 86.29202
## 6: 945.0026 124.64526
## 7: 981.2844 108.70527
## 8: 913.8924 123.97196
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 158.14, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6559143 0.6682142
## sample estimates:
## cor
## 0.6621088
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Strong correlkation. So in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 177.83, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6992585 0.7102801
## sample estimates:
## cor
## 0.7048118
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Slightly weaker correlation…
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDScar_kgco2e2018_pdw
## t = -119.05, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5613723 -0.5461891
## sample estimates:
## cor
## -0.5538267
## RUC11 mean_car_kgco2e
## 1: Rural town and fringe 2882.600
## 2: Rural town and fringe in a sparse setting 2198.057
## 3: Rural village and dispersed 3754.901
## 4: Rural village and dispersed in a sparse setting 3095.886
## 5: Urban city and town 2280.407
## 6: Urban city and town in a sparse setting 1761.591
## 7: Urban major conurbation 1718.983
## 8: Urban minor conurbation 1899.379
## mean_van_kgco2e
## 1: 412.7957
## 2: 346.9746
## 3: 664.4004
## 4: 586.5956
## 5: 379.2851
## 6: 300.1992
## 7: NA
## 8: 307.5766
Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSvan_kgco2e2018_pdw
## t = -0.79155, df = 32036, p-value = 0.4286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.015371712 0.006528074
## sample estimates:
## cor
## -0.004422349
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.0 315.0 390.0 434.2 503.0 6350.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 36.0 623.0 692.0 736.3 809.0 6351.0
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
The table below shows the overall £ GBP total for the case study area in £M.
## £m
## nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: 32039 107100.4 13847.3 5871.4
## £m
## region nLSOAs beis_GBPtotal_c beis_total_c_gas
## 1: South East 5278 20772.109 2272.0667
## 2: London 4826 19038.076 2048.9049
## 3: North West 4463 12703.630 1952.4218
## 4: East 3392 12199.273 1450.0085
## 5: West Midlands 3403 10191.715 1475.8156
## 6: South West 3059 9784.332 1147.1720
## 7: Yorkshire and The Humber 3271 9495.299 1494.0830
## 8: East Midlands 2713 8687.056 1249.5031
## 9: North East 1634 4228.912 757.3235
## beis_GBPtotal_c_elec
## 1: 1038.8330
## 2: 870.4882
## 3: 766.0786
## 4: 668.5684
## 5: 604.8308
## 6: 613.8662
## 7: 550.1641
## 8: 503.9008
## 9: 254.6698
The table below shows the mean per dwelling value rounded to the nearest £10.
## beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1: 4780 600 250
## beis_GBPtotal_c_energy_perdw
## 1: 850
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.7: £k per LSOA revenue using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 879 3172 4478 4775 5897 143661
LSOA11CD | LSOA01NM | WD18NM | IMD_Decile_label | CREDStotal_kgco2e_pdw | beis_GBPtotal_c_perdw |
E01031998 | Wiltshire 045C | Durrington and Larkhill | 9 | 586,372.2 | 143,661.19 |
E01009320 | Birmingham 081F | Sheldon | 4 | 364,668.7 | 89,343.84 |
E01033484 | Darlington 008F | Park East | 1 (10% most deprived) | 203,663.0 | 49,897.44 |
E01010151 | Solihull 026A | Knowle | 8 | 171,215.0 | 41,947.69 |
E01019556 | Chesterfield 010C | Holmebrook | 2 | 160,170.3 | 39,241.73 |
E01033749 | Liverpool 042F | Greenbank | 6 | 139,690.9 | 34,224.27 |
LSOA11CD | LSOA01NM | WD18NM | IMD_Decile_label | CREDStotal_kgco2e_pdw | beis_GBPtotal_c_perdw |
E01004562 | Wandsworth 002B | Queenstown | 3 | 4,965.387 | 1,216.5198 |
E01005133 | Manchester 013D | Ancoats & Beswick | 1 (10% most deprived) | 4,906.386 | 1,202.0645 |
E01008703 | Sunderland 013B | Hendon | 1 (10% most deprived) | 4,369.222 | 1,070.4593 |
E01015895 | Southend-on-Sea 010A | Victoria | 1 (10% most deprived) | 4,289.301 | 1,050.8787 |
E01033726 | Greenwich 034E | Eltham West | 2 | 3,808.630 | 933.1143 |
E01033583 | Newham 013G | Stratford and New Town | 3 | 3,587.624 | 878.9679 |
Figure ?? repeats the analysis but just for gas.
Now we see why Durrington & Larkhill stands out - either there is a decimal point error or there is a lot of ‘residential’ gas being used in the army camp and it’s all being allocated to one LSOA :-)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.96 499.22 596.50 604.03 702.83 17418.41
LSOA11CD | LSOA01NM | WD18NM | IMD_Decile_label | gasTCO2e_pdw | beis_GBPtotal_c_gas_perdw |
E01031998 | Wiltshire 045C | Durrington and Larkhill | 9 | 71.095556 | 17,418.411 |
E01000213 | Barnet 033F | Garden Suburb | 9 | 7.355417 | 1,802.077 |
E01023812 | Three Rivers 004A | Chorleywood North & Sarratt | 10 (10% least deprived) | 7.167803 | 1,756.112 |
E01023841 | Three Rivers 011C | Moor Park & Eastbury | 10 (10% least deprived) | 6.925828 | 1,696.828 |
E01004114 | Sutton 025D | Cheam | 10 (10% least deprived) | 6.721036 | 1,646.654 |
E01023813 | Three Rivers 004B | Chorleywood North & Sarratt | 10 (10% least deprived) | 6.718669 | 1,646.074 |
LSOA11CD | LSOA01NM | WD18NM | IMD_Decile_label | gasTCO2e_pdw | beis_GBPtotal_c_gas_perdw |
E01026645 | King's Lynn and West Norfolk 002A | Brancaster | 4 | 0.015725987 | 3.8528667 |
E01026718 | King's Lynn and West Norfolk 004D | Valley Hill | 5 | 0.014207424 | 3.4808188 |
E01027382 | Northumberland 002D | Norham and Islandshires | 4 | 0.013286252 | 3.2551318 |
E01020864 | County Durham 064G | Evenwood | 5 | 0.013174354 | 3.2277167 |
E01032746 | Southampton 029F | Bargate | 7 | 0.012818095 | 3.1404333 |
E01020534 | West Dorset 003F | Maiden Newton | 5 | 0.003918875 | 0.9601244 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.934 217.760 239.403 250.299 267.648 991.327
LSOA11CD | LSOA01NM | WD18NM | IMD_Decile_label | elecTCO2e_pdw | beis_GBPtotal_c_elec_perdw |
E01000206 | Barnet 033B | Garden Suburb | 8 | 4.046235 | 991.3275 |
E01030692 | Runnymede 005D | Virginia Water | 9 | 3.360000 | 823.2000 |
E01030342 | Elmbridge 018B | Oxshott and Stoke D'Abernon | 10 (10% least deprived) | 3.346058 | 819.7842 |
E01030346 | Elmbridge 016A | Weybridge St George's Hill | 8 | 3.280690 | 803.7690 |
E01004690 | Westminster 019D | Knightsbridge and Belgravia | 10 (10% least deprived) | 2.875978 | 704.6145 |
E01003465 | Merton 002D | Village | 10 (10% least deprived) | 2.873194 | 703.9325 |
LSOA11CD | LSOA01NM | WD18NM | IMD_Decile_label | elecTCO2e_pdw | beis_GBPtotal_c_elec_perdw |
E01008777 | Sunderland 026C | St Chad's | 1 (10% most deprived) | 0.50798472 | 124.456256 |
E01024604 | Swale 014C | St Ann's | 2 | 0.48269076 | 118.259237 |
E01002862 | Kensington and Chelsea 014E | Stanley | 4 | 0.45468354 | 111.397468 |
E01033736 | Greenwich 004H | Woolwich Riverside | 8 | 0.43406378 | 106.345626 |
E01004739 | Westminster 024E | Tachbrook | 8 | 0.33211144 | 81.367302 |
E01010257 | Walsall 007E | Aldridge North and Walsall Wood | 9 | 0.04054826 | 9.934324 |
Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 112.4 729.7 832.7 854.3 954.3 17811.1
Applied at to per dwelling values (not LSOA total)
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 3587.624 12947.165 18275.816 24069.709 586372.222
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw
## 1: 20.14367 1579.554 1305.5194
## 2: 18.65717 1579.554 1305.5194
## 3: 12.73055 1553.127 0.0000
## 4: 19.87204 1579.554 1305.5194
## 5: 28.94094 1579.554 1305.5194
## 6: 15.12282 1579.554 533.0367
## 7: 20.44254 1579.554 1305.5194
## 8: 21.00183 1579.554 1305.5194
## 9: 11.91349 1453.446 0.0000
## 10: 27.68047 1579.554 1305.5194
## beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
## 1: 685.5016 3570.575
## 2: 139.9562 3025.030
## 3: 0.0000 1553.127
## 4: 585.8127 3470.886
## 5: 3914.1019 6799.175
## 6: 0.0000 2112.591
## 7: 795.1884 3680.262
## 8: 1000.4491 3885.523
## 9: 0.0000 1453.446
## 10: 3451.5071 6336.581
| Name | …[] |
| Number of rows | 32039 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 19.49 | 9.19 | 3.59 | 12.95 | 18.28 | 24.07 | 586.37 | ▇▁▁▁▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3727.91 | 3031.87 | 437.69 | 1579.54 | 2885.00 | 5011.43 | 211376.45 | ▇▁▁▁▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2528423.89 | 1653321.47 | 543095.20 | 1222262.12 | 2220433.98 | 3356216.64 | 84377314.16 | ▇▁▁▁▁ |
## nLSOAs sum_total_sc1 sum_total_sc2
## 1: 32039 107100.4 81008.17
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1: 130.9338 15.97393
## 2: 82.7677 10.09766
## 3: 717.3671 87.51878
## 4: 1041.0619 127.00956
## 5: 2480.0943 248.59012
## 6: 793.2446 96.77584
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1: 130.9338 15.97393 0.00000
## 2: 82.7677 10.09766 0.00000
## 3: 717.3671 87.51878 0.00000
## 4: 1041.0619 127.00956 0.00000
## 5: 2480.0943 248.59012 97.27961
## 6: 793.2446 96.77584 0.00000
## beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1: 0.00000 15.97393
## 2: 0.00000 10.09766
## 3: 0.00000 87.51878
## 4: 0.00000 127.00956
## 5: 16.66576 362.53549
## 6: 0.00000 96.77584
## [1] 9086.681
## Saving 7 x 5 in image
## [1] 3997.045
## Saving 7 x 5 in image
## £m
## nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP
## 1: 32039 81008.17 9086.681 3997.045
## sumPop
## 1: 54619583
## £m
## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP
## 1: South East 5278 17507.695 1503.8874
## 2: London 4826 15649.748 1389.3724
## 3: East 3392 9526.267 939.0531
## 4: North West 4463 8777.331 1282.6361
## 5: West Midlands 3403 7270.355 976.6076
## 6: South West 3059 6809.964 653.9993
## 7: Yorkshire and The Humber 3271 6504.827 1011.0844
## 8: East Midlands 2713 6247.711 827.1231
## 9: North East 1634 2714.274 502.9172
## sumElecEmissions_GBP sumPop
## 1: 766.6684 8973952
## 2: 583.7890 8889572
## 3: 487.3759 5818700
## 4: 491.5296 7236660
## 5: 410.8372 5765703
## 6: 431.0802 5213266
## 7: 342.8749 5405939
## 8: 339.4664 4693551
## 9: 143.4232 2622240
fromAE <- 13300 fromFG <- 26800
Excludes EPC A, B & C (assumes no need to upgrade)
## To retrofit D-E (£m)
## [1] 177847.9
## Number of dwellings: 13372024
## To retrofit F-G (£m)
## [1] 26752.52
## Number of dwellings: 998229
## To retrofit D-G (£m)
## [1] 204600.4
## To retrofit D-G (mean per dwelling)
## [1] 14163.45
## meanPerLSOA_GBPm total_GBPm
## 1: 6.385981 204600.4
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.09599 2.40262 3.17210 3.53055 4.44332 15.64765 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7742 14.7584 16.8635 17.5709 19.2684 118.6634 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## Highest retofit sum cost
## LSOA11CD LSOA11NM WD18NM
## 1: E01019012 Cornwall 054E St Ives East
## 2: E01018781 Cornwall 034B Rame Peninsular
## 3: E01027840 Scarborough 002C Mulgrave
## 4: E01021988 Tendring 018A Golf Green
## 5: E01018766 Cornwall 028D Looe West, Lansallos and Lanteglos
## 6: E01020541 West Dorset 002C Sherborne East
## 7: E01026741 North Norfolk 004A High Heath
## 8: E01019002 Cornwall 070B Newlyn and Mousehole
## 9: E01018982 Cornwall 057C Hayle North
## 10: E01027374 Northumberland 003A Bamburgh
## retrofitSum yearsToPay epc_D_pc epc_E_pc epc_F_pc epc_G_pc
## 1: 26389383 32.33181 0.2881890 0.2251969 0.1314961 0.07086614
## 2: 22060172 49.74697 0.2993730 0.2664577 0.2335423 0.10971787
## 3: 21959636 32.73446 0.2821317 0.2272727 0.2163009 0.10031348
## 4: 21701517 36.61775 0.2955900 0.3313468 0.1620977 0.14302741
## 5: 21409249 46.57111 0.2181070 0.2716049 0.2935528 0.10973937
## 6: 21066562 31.46956 0.3038793 0.3232759 0.2090517 0.06896552
## 7: 20793004 35.15440 0.2971888 0.2650602 0.1994645 0.06827309
## 8: 20415414 45.01341 0.1710963 0.2807309 0.3089701 0.17940199
## 9: 20411151 40.50980 0.2675386 0.1819263 0.2092747 0.14030916
## 10: 19563519 29.95735 0.3329532 0.2257697 0.1402509 0.05131129
## Saving 7 x 5 in image
What happens in Year 2 totally depends on the rate of upgrades…
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.06524 2.83119 4.88954 5.92627 8.83376 31.42356 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7742 14.7584 16.8635 17.5709 19.2684 118.6634 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Saving 7 x 5 in image
## Saving 7 x 5 in image
What happens in Year 2 totally depends on the rate of upgrades…
## Saving 7 x 5 in image
I don’t know if this will work…
## Doesn't